Prediction model for the efficacy of folic acid therapy on hyperhomocysteinaemia based on genetic risk score methods

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma

The identification of biomarkers for predicting inter-individual sorafenib response variability could allow hepatocellular carcinoma (HCC) patient stratification. SNPs in angiogenesis- and drug absorption, distribution, metabolism, and excretion (ADME)-related genes were evaluated to identify new potential predictive biomarkers of sorafenib response in HCC patients. Five known SNPs in angiogenesis-related genes, including VEGF-A, VEGF-C, HIF-1a, ANGPT2, and NOS3, were investigated in 34 HCC patients (9 sorafenib responders and 25 non-responders). A subgroup of 23 patients was genotyped for SNPs in ADME genes. A machine learning classifier method was used to discover classification rules for our dataset. We found that only the VEGF-A (rs2010963) C allele and CC genotype were significantly associated with sorafenib response. ADME-related gene analysis identified 10 polymorphic variants in ADH1A (rs6811453), ADH6 (rs10008281), SULT1A2/CCDC101 (rs11401), CYP26A1 (rs7905939), DPYD (rs2297595 and rs1801265), FMO2 (rs2020863), and SLC22A14 (rs149738, rs171248, and rs183574) significantly associated with sorafenib response. We have identified a genetic signature of predictive response that could permit non-responder/responder patient stratification. Angiogenesis- and ADME-related genes correlation was confirmed by cumulative genetic risk score and network and pathway enrichment analysis. Our findings provide a proof of concept that needs further validation in follow-up studies for HCC patient stratification for sorafenib prescription.

Using the optimal method-explained variance weighted genetic risk score to predict the efficacy of folic acid therapy to hyperhomocysteinemia

BACKGROUND

Genetic risk score (GRS) is a useful way to explore genetic architectures and the relationships of complex diseases. Several studies had revealed many single nucleotide polymorphisms (SNPs) associated with the efficacy of folic acid treatment to hyperhomocysteinemia (HHcy).

METHODS

We aimed to construct and screen out the optimal predictive model based on four GRSs and traditional risk factors. Four GRSs enrolled four SNPs (MTHFR rs1801131, MTHFR rs1801133, MTRR rs1801394, BHMT rs3733890) were presented as follows: (a) simple count genetic risk score (SC-GRS), (b) direct logistic regression genetic risk score (DL-GRS), (c) polygenic genetic risk score (PG-GRS), and (d) explained variance weighted genetic risk score (EV-GRS). We performed a prospective cohort study including 638 HHcy patients. Then we evaluated the associations of four GRSs with folic acid's efficacy and the performance of four GRSs.

RESULTS

Four GRSs were independently associated with efficacy of treatment (p < 0.05). When combining GRSs with traditional risk factors, the AUC of the four models were all above 0.900 in the training set (Tradition + SC-GRS: 0.909, Tradition + DL-GRS: 0.909, Tradition + PG-GRS: 0.904, Tradition + EV-GRS: 0.910). And EV-GRS got the highest AUC. When evaluating the models in the testing set, we got the same conclusion that EV-GRS was optimal among four GRSs with the highest AUC (0.878) and the highest increase of AUC (0.008).

CONCLUSION

A more precise predictive model combing the optimal GRS with traditional risk factors was constructed to predict the efficacy of folic acid therapy to HHcy.

Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia

Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954-5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092-7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden's index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden's index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.